Reduced antibody cross-reactivity following infection with B.1.1.7 than with parental SARS-CoV-2 strains.

Background: The degree of heterotypic immunity induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains is a major determinant of the spread of emerging variants and the success of vaccination campaigns, but remains incompletely understood. Methods: We examined the immunogenicity of SARS-CoV-2 variant B.1.1.7 (Alpha) that arose in the United Kingdom and spread globally. We determined titres of spike glycoprotein-binding antibodies and authentic virus neutralising antibodies induced by B.1.1.7 infection to infer homotypic and heterotypic immunity. Results: Antibodies elicited by B.1.1.7 infection exhibited significantly reduced recognition and neutralisation of parental strains or of the South Africa variant B.1.351 (Beta) than of the infecting variant. The drop in cross-reactivity was significantly more pronounced following B.1.1.7 than parental strain infection. Conclusions: The results indicate that heterotypic immunity induced by SARS-CoV-2 variants is asymmetric. Funding: This work was supported by the Francis Crick Institute and the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg.

High content phenotypic screening identifies serotonin receptor modulators with selective activity upon breast cancer cell cycle and cytokine signaling pathways.

Heterogeneity in disease mechanisms between genetically distinct patients contributes to high attrition rates in late stage clinical drug development. New personalized medicine strategies aim to identify predictive biomarkers which stratify patients most likely to respond to a particular therapy. However, for complex multifactorial diseases not characterized by a single genetic driver, empirical approaches to identifying predictive biomarkers and the most promising therapies for personalized medicine are required. In vitro pharmacogenomics seeks to correlate in vitro drug sensitivity testing across panels of genetically distinct cell models with genomic, gene expression or proteomic data to identify predictive biomarkers of drug response. However, the vast majority of in vitro pharmacogenomic studies performed to date are limited to dose-response screening upon a single viability assay endpoint. In this article we describe the application of multiparametric high content phenotypic screening and the theta comparative cell scoring method to quantify and rank compound hits, screened at a single concentration, which induce a broad variety of divergent phenotypic responses between distinct breast cancer cell lines. High content screening followed by transcriptomic pathway analysis identified serotonin receptor modulators which display selective activity upon breast cancer cell cycle and cytokine signaling pathways correlating with inhibition of cell growth and survival. These methods describe a new evidence-led approach to rapidly identify compounds which display distinct response between different cell types. The results presented also warrant further investigation of the selective activity of serotonin receptor modulators upon breast cancer cell growth and survival as a potential drug repurposing opportunity.

Drug Screening Platforms and RPPA.

Since its inception as a scalable and cost-effective method for precise quantification of the abundance of multiple protein analytes and post-translational epitopes across large sample sets, reverse phase protein array (RPPA) has been utilized as a drug discovery tool. Key RPPA drug discovery applications include primary screening of abundance or activation state of nominated protein targets, secondary screening for toxicity and selectivity, mechanism-of-action profiling, biomarker discovery, and drug combination discovery. In recent decades, drug discovery strategies have evolved dramatically in response to continual advances in technology platforms supporting high-throughput screening, structure-based drug design, new therapeutic modalities, and increasingly more complex and disease-relevant cell-based and in vivo preclinical models of disease. Advances in biological laboratory capabilities in drug discovery are complemented by significant developments in bioinformatics and computational approaches for integrating large complex datasets. Bioinformatic and computational analysis of integrated molecular, pathway network and phenotypic datasets enhance multiple stages of the drug discovery process and support more informative drug target hypothesis generation and testing. In this chapter we discuss and present examples demonstrating how the latest advances in RPPA complement and integrate with other emerging drug screening platforms to support a new era of more informative and evidence-led drug discovery strategies.

Evaluation of Machine Learning Classifiers to Predict Compound Mechanism of Action When Transferred across Distinct Cell Lines.

Multiparametric high-content imaging assays have become established to classify cell phenotypes from functional genomic and small-molecule library screening assays. Several groups have implemented machine learning classifiers to predict the mechanism of action of phenotypic hit compounds by comparing the similarity of their high-content phenotypic profiles with a reference library of well-annotated compounds. However, the majority of such examples are restricted to a single cell type often selected because of its suitability for simple image analysis and intuitive segmentation of morphological features. The aim of the current study was to evaluate and compare the performance of a classic ensemble-based tree classifier trained on extracted morphological features and a deep learning classifier using convolutional neural networks (CNNs) trained directly on images from the same dataset to predict compound mechanism of action across a morphologically and genetically distinct cell panel. Our results demonstrate that application of a CNN classifier delivers equivalent accuracy compared with an ensemble-based tree classifier at compound mechanism of action prediction within cell lines. However, our CNN analysis performs worse than an ensemble-based tree classifier when trained on multiple cell lines at predicting compound mechanism of action on an unseen cell line.

The fungal alkaloid Okaramine-B activates an L-glutamate-gated chloride channel from Ixodes scapularis, a tick vector of Lyme disease.

A novel L-glutamate-gated anion channel (IscaGluCl1) has been cloned from the black-legged tick, Ixodes scapularis, which transmits multiple pathogens including the agents of Lyme disease and human granulocytic anaplasmosis. When mRNA encoding IscaGluCl1 was expressed in Xenopus laevis oocytes, we detected robust 50-400 nA currents in response to 100 µM L-glutamate. Responses to L-glutamate were concentration-dependent (pEC50 3.64 ±â€¯0.11). Ibotenate was a partial agonist on IscaGluCl1. We detected no response to 100 µM aspartate, quisqualate, kainate, AMPA or NMDA. Ivermectin at 1 µM activated IscaGluCl1, whereas picrotoxinin (pIC50 6.20 ±â€¯0.04) and the phenylpyrazole fipronil (pIC50 6.90 ±â€¯0.04) showed concentration-dependent block of the L-glutamate response. The indole alkaloid okaramine B, isolated from fermentation products of Penicillium simplicissimum (strain AK40) grown on okara pulp, activated IscaGluCl1 in a concentration-dependent manner (pEC50 5.43 ±â€¯0.43) and may serve as a candidate lead compound for the development of new acaricides.

High-Dimensional Profiling: The Theta Comparative Cell Scoring Method.

Principal component analysis enables dimensional reduction of multivariate datasets that are typical in high-content screening. A common analysis utilizing principal components is a distance measurement between a perturbagen-such as small-molecule treatment or shRNA knockdown-and a negative control. This method works well to identify active perturbagens, though it cannot discern between distinct phenotypic responses. Here, we describe an extension of the principal component analysis approach to multivariate high-content screening data to enable quantification of differences in direction in principal component space. The theta comparative cell scoring method can identify and quantify differential phenotypic responses between panels of cell lines to small-molecule treatment to support in vitro pharmacogenomics and drug mechanism-of-action studies.

Data-analysis strategies for image-based cell profiling.

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

Development of the Theta Comparative Cell Scoring Method to Quantify Diverse Phenotypic Responses Between Distinct Cell Types.

In this article, we have developed novel data visualization tools and a Theta comparative cell scoring (TCCS) method, which supports high-throughput in vitro pharmacogenomic studies across diverse cellular phenotypes measured by multiparametric high-content analysis. The TCCS method provides a univariate descriptor of divergent compound-induced phenotypic responses between distinct cell types, which can be used for correlation with genetic, epigenetic, and proteomic datasets to support the identification of biomarkers and further elucidate drug mechanism-of-action. Application of these methods to compound profiling across high-content assays incorporating well-characterized cells representing known molecular subtypes of disease supports the development of personalized healthcare strategies without prior knowledge of a drug target. We present proof-of-principle data quantifying distinct phenotypic response between eight breast cancer cells representing four disease subclasses. Application of the TCCS method together with new advances in next-generation sequencing, induced pluripotent stem cell technology, gene editing, and high-content phenotypic screening are well placed to advance the identification of predictive biomarkers and personalized medicine approaches across a broader range of disease types and therapeutic classes.

Next-generation phenotypic screening.

Phenotypic drug discovery (PDD) strategies are defined by screening and selection of hit or lead compounds based on quantifiable phenotypic endpoints without prior knowledge of the drug target. We outline the challenges associated with traditional phenotypic screening strategies and propose solutions and new opportunities to be gained by adopting modern PDD technologies. We highlight both historical and recent examples of approved drugs and new drug candidates discovered by modern phenotypic screening. Finally, we offer a prospective view of a new era of PDD underpinned by a wealth of technology advances in the areas of in vitro model development, high-content imaging and image informatics, mechanism-of-action profiling and target deconvolution.